Title :
Video transcoding time prediction for proactive load balancing
Author :
Deneke, T. ; Haile, Habtegebreil ; Lafond, S. ; Lilius, Johan
Author_Institution :
Abo Akad. Univ., Abo, Finland
Abstract :
In this paper, we present a method for predicting the transcoding time of videos given an input video stream and its transcoding parameters. Video transcoding time is treated as a random variable and is statistically predicted from past observations. Our proposed method predicts the transcoding time as a function of several parameters of the input and output video streams, and does not require any detailed information about the codec used. We show the effectiveness of our method via comparing the resulting predictions with the actual transcoding times on unseen video streams. Simulation results show that our prediction method enables a significantly better load balancing of transcoding jobs than classical load balancing methods.
Keywords :
prediction theory; resource allocation; transcoding; video coding; video streaming; input video stream; proactive load balancing; video transcoding time prediction; Bit rate; Codecs; Load management; Load modeling; Predictive models; Transcoding; YouTube; Load Balancing; Machine Learning; Prediction; Transcoding;
Conference_Titel :
Multimedia and Expo (ICME), 2014 IEEE International Conference on
Conference_Location :
Chengdu
DOI :
10.1109/ICME.2014.6890256